Managing Constraints and Preferences for Winner Determination in Multi-attribute Reverse Auctions
Bibliographic record
Abstract
Multi-Attribute Reverse Auctions (MARAs) are considered an excellent way to buy and sell efficiently. However, eliciting the buyer's requirements and preferences as well as determining the winner, are both challenging tasks. In this paper, we propose a multi-round and semi-sealed MARA auction system, capable of determining the winner given a set of user's preferences and requirements. This system is capable of managing qualitative, quantitative and conditional preferences together with constraints. For that, we use the constrained Tradeoffs-enhanced Conditional Preference Networks (constrained TCP-nets) graphical model for representing constraints as well as qualitative and conditional preferences, and Multi-Attribute Utility Theory (MAUT) for dealing with quantitative preferences. Determining the winners of the auction will then be achieved using the backtrack search algorithm we use for solving constrained TCP-nets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".